import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


_SCREAMING_SNAKE_CASE 		=      datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
     import pyspark
@dataclass
class SCREAMING_SNAKE_CASE_		(      datasets.BuilderConfig		):
   __lowerCAmelCase           =				None





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					)		->	List[str]:







 '''simple docstring'''
 import pyspark

 def generate_fn():
  UpperCamelCase			  =      df.select("""*"""							,					pyspark.sql.functions.spark_partition_id().alias("""part_id"""       )       )
  for partition_id in partition_order:
   UpperCamelCase			  =      df_with_partition_id.select("""*"""       ).where(f"""part_id = {partition_id}"""       ).drop("""part_id"""       )
   UpperCamelCase			  =      partition_df.collect()
   UpperCamelCase			  =      0
   for row in rows:
    yield f"""{partition_id}_{row_id}""", row.asDict()
    row_id += 1

 return generate_fn
class SCREAMING_SNAKE_CASE_		(      _BaseExamplesIterable		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						"pyspark.sql.DataFrame"   ,   lowerCamelCase_						:						Tuple=None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      df
    UpperCamelCase			  =      partition_order or range(self.df.rdd.getNumPartitions()				)
    UpperCamelCase			  =      _generate_iterable_examples(self.df   ,   self.partition_order				)







   def __iter__(     self						:						int				):





    """simple docstring"""


    yield from self.generate_examples_fn()







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						np.random.Generator				):





    """simple docstring"""


    UpperCamelCase			  =      list(range(self.df.rdd.getNumPartitions()				)				)
    generator.shuffle(lowerCamelCase_				)
    return SparkExamplesIterable(self.df   ,   partition_order=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.split_shard_indices_by_worker(lowerCamelCase_   ,   lowerCamelCase_				)
    return SparkExamplesIterable(self.df   ,   partition_order=lowerCamelCase_				)







   @property
   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    return len(self.partition_order				)





class SCREAMING_SNAKE_CASE_		(      datasets.DatasetBuilder		):
   __lowerCAmelCase           =				SparkConfig







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						"pyspark.sql.DataFrame"   ,   lowerCamelCase_						:						str = None   ,   lowerCamelCase_						:						str = None   ,   **lowerCamelCase_						:						Union[str, Any]   ,   ):





    """simple docstring"""


    import pyspark

    UpperCamelCase			  =      pyspark.sql.SparkSession.builder.getOrCreate()
    UpperCamelCase			  =      df
    UpperCamelCase			  =      working_dir

    super().__init__(
        cache_dir=lowerCamelCase_   ,   config_name=str(self.df.semanticHash()				)   ,   **lowerCamelCase_   ,   )







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    def create_cache_and_write_probe(lowerCamelCase_						:						Dict				):
     # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
     # already exist.
     os.makedirs(self._cache_dir   ,   exist_ok=lowerCamelCase_				)
     UpperCamelCase			  =      os.path.join(self._cache_dir   ,   """fs_test""" + uuid.uuida().hex				)
     # Opening the file in append mode will create a new file unless it already exists, in which case it will not
     # change the file contents.
     open(lowerCamelCase_   ,   """a"""				)
     return [probe_file]

    if self._spark.conf.get("""spark.master"""   ,   """"""				).startswith("""local"""				):
     return

    # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
    # accessible to the driver.
    # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
    if self._cache_dir:
     UpperCamelCase			  =      (
         self._spark.sparkContext.parallelize(range(1				)   ,   1				).mapPartitions(lowerCamelCase_				).collect()
     )
     if os.path.isfile(probe[0]				):
      return

    raise ValueError(
        """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return datasets.DatasetInfo(features=self.config.features				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						datasets.download.download_manager.DownloadManager				):





    """simple docstring"""


    return [datasets.SplitGenerator(name=datasets.Split.TRAIN				)]







   def 						lowerCamelCase_	(     self						:						Any   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    import pyspark

    def get_arrow_batch_size(lowerCamelCase_						:						int				):
     for batch in it:
      yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]}				)

    UpperCamelCase			  =      self.df.count()
    UpperCamelCase			  =      df_num_rows if df_num_rows <= 100 else 100
    # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
    UpperCamelCase			  =      (
        self.df.limit(lowerCamelCase_				)
        .repartition(1				)
        .mapInArrow(lowerCamelCase_   ,   """batch_bytes: long"""				)
        .agg(pyspark.sql.functions.sum("""batch_bytes"""				).alias("""sample_bytes"""				)				)
        .collect()[0]
        .sample_bytes
        / sample_num_rows
    )
    UpperCamelCase			  =      approx_bytes_per_row * df_num_rows
    if approx_total_size > max_shard_size:
     # Make sure there is at least one row per partition.
     UpperCamelCase			  =      min(lowerCamelCase_   ,   int(approx_total_size / max_shard_size				)				)
     UpperCamelCase			  =      self.df.repartition(lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						List[str]   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						str   ,   lowerCamelCase_						:						int   ,   ):





    """simple docstring"""


    import pyspark

    UpperCamelCase			  =      ParquetWriter if file_format == """parquet""" else ArrowWriter
    UpperCamelCase			  =      os.path.join(self._working_dir   ,   os.path.basename(lowerCamelCase_				)				) if self._working_dir else fpath
    UpperCamelCase			  =      file_format == """parquet"""

    # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
    # pickling the SparkContext.
    UpperCamelCase			  =      self.config.features
    UpperCamelCase			  =      self._writer_batch_size
    UpperCamelCase			  =      self._fs.storage_options

    def write_arrow(lowerCamelCase_						:						List[Any]				):
     # Within the same SparkContext, no two task attempts will share the same attempt ID.
     UpperCamelCase			  =      pyspark.TaskContext().taskAttemptId()
     UpperCamelCase			  =      next(lowerCamelCase_   ,   lowerCamelCase_				)
     if first_batch is None:
      # Some partitions might not receive any data.
      return pa.RecordBatch.from_arrays(
          [[task_id], [0], [0]]   ,   names=["""task_id""", """num_examples""", """num_bytes"""]   ,   )
     UpperCamelCase			  =      0
     UpperCamelCase			  =      writer_class(
         features=lowerCamelCase_   ,   path=working_fpath.replace("""SSSSS"""   ,   f"""{shard_id:05d}"""				).replace("""TTTTT"""   ,   f"""{task_id:05d}"""				)   ,   writer_batch_size=lowerCamelCase_   ,   storage_options=lowerCamelCase_   ,   embed_local_files=lowerCamelCase_   ,   )
     UpperCamelCase			  =      pa.Table.from_batches([first_batch]				)
     writer.write_table(lowerCamelCase_				)
     for batch in it:
      if max_shard_size is not None and writer._num_bytes >= max_shard_size:
       UpperCamelCase  ,     UpperCamelCase			  =      writer.finalize()
       writer.close()
       yield pa.RecordBatch.from_arrays(
           [[task_id], [num_examples], [num_bytes]]   ,   names=["""task_id""", """num_examples""", """num_bytes"""]   ,   )
       shard_id += 1
       UpperCamelCase			  =      writer_class(
           features=writer._features   ,   path=working_fpath.replace("""SSSSS"""   ,   f"""{shard_id:05d}"""				).replace("""TTTTT"""   ,   f"""{task_id:05d}"""				)   ,   writer_batch_size=lowerCamelCase_   ,   storage_options=lowerCamelCase_   ,   embed_local_files=lowerCamelCase_   ,   )
      UpperCamelCase			  =      pa.Table.from_batches([batch]				)
      writer.write_table(lowerCamelCase_				)

     if writer._num_bytes > 0:
      UpperCamelCase  ,     UpperCamelCase			  =      writer.finalize()
      writer.close()
      yield pa.RecordBatch.from_arrays(
          [[task_id], [num_examples], [num_bytes]]   ,   names=["""task_id""", """num_examples""", """num_bytes"""]   ,   )

     if working_fpath != fpath:
      for file in os.listdir(os.path.dirname(lowerCamelCase_				)				):
       UpperCamelCase			  =      os.path.join(os.path.dirname(lowerCamelCase_				)   ,   os.path.basename(lowerCamelCase_				)				)
       shutil.move(lowerCamelCase_   ,   lowerCamelCase_				)

    UpperCamelCase			  =      (
        self.df.mapInArrow(lowerCamelCase_   ,   """task_id: long, num_examples: long, num_bytes: long"""				)
        .groupBy("""task_id"""				)
        .agg(
            pyspark.sql.functions.sum("""num_examples"""				).alias("""total_num_examples"""				)   ,   pyspark.sql.functions.sum("""num_bytes"""				).alias("""total_num_bytes"""				)   ,   pyspark.sql.functions.count("""num_bytes"""				).alias("""num_shards"""				)   ,   pyspark.sql.functions.collect_list("""num_examples"""				).alias("""shard_lengths"""				)   ,   )
        .collect()
    )
    for row in stats:
     yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)







   def 						lowerCamelCase_	(     self						:						Dict   ,   lowerCamelCase_						:						"datasets.SplitGenerator"   ,   lowerCamelCase_						:						str = "arrow"   ,   lowerCamelCase_						:						Optional[Union[str, int]] = None   ,   lowerCamelCase_						:						Optional[int] = None   ,   **lowerCamelCase_						:						Tuple   ,   ):





    """simple docstring"""


    self._validate_cache_dir()

    UpperCamelCase			  =      convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE				)
    self._repartition_df_if_needed(lowerCamelCase_				)
    UpperCamelCase			  =      not is_remote_filesystem(self._fs				)
    UpperCamelCase			  =      os.path.join if is_local else posixpath.join

    UpperCamelCase			  =      """-TTTTT-SSSSS-of-NNNNN"""
    UpperCamelCase			  =      f"""{self.name}-{split_generator.name}{SUFFIX}.{file_format}"""
    UpperCamelCase			  =      path_join(self._output_dir   ,   lowerCamelCase_				)

    UpperCamelCase			  =      0
    UpperCamelCase			  =      0
    UpperCamelCase			  =      0
    UpperCamelCase			  =      []
    UpperCamelCase			  =      []

    for task_id, content in self._prepare_split_single(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				):
     (
         (
         UpperCamelCase
     )  ,     (
         UpperCamelCase
     )  ,     (
         UpperCamelCase
     )  ,     (
         UpperCamelCase
     )  ,     
     )			  =      content
     if num_bytes > 0:
      total_num_examples += num_examples
      total_num_bytes += num_bytes
      total_shards += num_shards
      task_id_and_num_shards.append((task_id, num_shards)				)
      all_shard_lengths.extend(lowerCamelCase_				)

    UpperCamelCase			  =      total_num_examples
    UpperCamelCase			  =      total_num_bytes

    # should rename everything at the end
    logger.debug(f"""Renaming {total_shards} shards."""				)
    if total_shards > 1:
     UpperCamelCase			  =      all_shard_lengths

     # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
     # pickling error due to pickling the SparkContext.
     UpperCamelCase			  =      self._fs

     # use the -SSSSS-of-NNNNN pattern
     def _rename_shard(
         lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						int   ,   ):
      rename(
          lowerCamelCase_   ,   fpath.replace("""SSSSS"""   ,   f"""{shard_id:05d}"""				).replace("""TTTTT"""   ,   f"""{task_id:05d}"""				)   ,   fpath.replace("""TTTTT-SSSSS"""   ,   f"""{global_shard_id:05d}"""				).replace("""NNNNN"""   ,   f"""{total_shards:05d}"""				)   ,   )

     UpperCamelCase			  =      []
     UpperCamelCase			  =      0
     for i in range(len(lowerCamelCase_				)				):
      UpperCamelCase  ,     UpperCamelCase			  =      task_id_and_num_shards[i]
      for shard_id in range(lowerCamelCase_				):
       args.append([task_id, shard_id, global_shard_id]				)
       global_shard_id += 1
     self._spark.sparkContext.parallelize(lowerCamelCase_   ,   len(lowerCamelCase_				)				).map(lambda lowerCamelCase_				: _rename_shard(*lowerCamelCase_				)				).collect()
    else:
     # don't use any pattern
     UpperCamelCase			  =      0
     UpperCamelCase			  =      task_id_and_num_shards[0][0]
     self._rename(
         fpath.replace("""SSSSS"""   ,   f"""{shard_id:05d}"""				).replace("""TTTTT"""   ,   f"""{task_id:05d}"""				)   ,   fpath.replace(lowerCamelCase_   ,   """"""				)   ,   )







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						"datasets.SplitGenerator"   ,   ):





    """simple docstring"""


    return SparkExamplesIterable(self.df				)

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
